Methodology
To independently verify and provide a confidence assessment of the EA system’s trading plan, I will use the following technical analysis methods:
- Moving Averages (MA): Calculate the 5-period Moving Average (MA(5)) and the 288-period Bollinger Bands.
- Bollinger Bands (BB): Calculate the 288-period Bollinger Bands with a 2 standard deviation (SD) width.
- Trend Analysis: Analyze the direction and strength of the trend using the MA(5) and BB.
- Support and Resistance Levels: Identify key support and resistance levels based on recent price action.
- Market Session Considerations: Account for the specific market session characteristics (e.g., Asian, London, New York sessions).
Data Preparation
First, let’s parse the provided K-line data and calculate the necessary indicators.
`python
import pandas as pd
import numpy as np
Parse the data
data = [
“2026.02.04 02:50,4902.67000,4902.67000,4902.67000,4902.67000,1”,
# … (all 432 bars)
“2026.02.02 13:55,4537.69000,4565.67000,4521.13000,4549.76000,2768”
]
Convert to DataFrame
df = pd.DataFrame([line.split(‘,’) for line in data], columns=[‘Timestamp’, ‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’])
df[‘Timestamp’] = pd.to_datetime(df[‘Timestamp’], format=’%Y.%m.%d %H:%M’)
df[[‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’]] = df[[‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’]].astype(float)
Calculate 5-period Moving Average
df[‘MA_5’] = df[‘Close’].rolling(window=5).mean()
Calculate 288-period Bollinger Bands
df[‘BB_Middle’] = df[‘Close’].rolling(window=288).mean()
df[‘BB_Std’] = df[‘Close’].rolling(window=288).std()
df[‘BB_Upper’] = df[‘BB_Middle’] + 2 * df[‘BB_Std’]
df[‘BB_Lower’] = df[‘BB_Middle’] – 2 * df[‘BB_Std’]
Latest Close Price
latest_close = df[‘Close’].iloc[-1]
`
Trend Analysis
- Moving Average (MA(5)): The 5-period moving average is used to identify short-term trends.
- Bollinger Bands (BB): The 288-period Bollinger Bands are used to identify volatility and potential breakout points.
Market State Verification
- Current Market Session: The current timestamp is 02:50 UTC+8, which falls within the low liquidity period (02:00-06:00).
- Trend Direction: Check if the MA(5) is trending downward and if it has broken through the upper Bollinger Band.
`python
Check the latest values
latest_ma_5 = df[‘MA_5’].iloc[-1]
latest_bb_upper = df[‘BB_Upper’].iloc[-1]
Determine the trend
if latest_ma_5 df[‘BB_Upper’].iloc[-2]:
ma_trending_downward = True
else:
ma_trending_downward = False
Output the results
print(f”Latest Close: {latest_close}”)
print(f”Latest MA(5): {latest_ma_5}”)
print(f”Latest BB Upper: {latest_bb_upper}”)
print(f”MA(5) Trending Downward: {ma_trending_downward}”)
`
Support and Resistance Levels
- Support Level: Use the recent low as a support level.
- Resistance Level: Use the recent high as a resistance level.
`python
Identify support and resistance levels
support_level = df[‘Low’].iloc[-20:].min()
resistance_level = df[‘High’].iloc[-20:].max()
print(f”Support Level: {support_level}”)
print(f”Resistance Level: {resistance_level}”)
`
Final Trading Signal
- Direction Signal: Based on the trend analysis, determine the direction signal.
- Signal Strength: Assign a signal strength based on the confidence in the trend.
- Stop-Loss and Take-Profit Levels: Set these based on the support and resistance levels.
`python
Determine the final trading signal
if ma_trending_downward:
direction_signal = “Short”
signal_strength = -5 # Moderate confidence
trade_entry_price = latest_close
stop_loss_price = resistance_level
take_profit_price = support_level
else:
direction_signal = “Watch”
signal_strength = 0
support_level = df[‘Low’].iloc[-20:].min()
resistance_level = df[‘High’].iloc[-20:].max()
Output the final trading signal
if direction_signal == “Watch”:
print(f”Direction signal: Watch”)
print(f”Latest Close: >>> {latest_close} <<<")
print(f”Signal Strength: =>> {signal_strength} <<= ")
print(f"Support level: <span class="support"> {support_level} </span>")
print(f"Resistance level: <span class="resistance"> {resistance_level} </span>")
elif direction_signal == “Short”:
print(f”Direction signal: Short”)
print(f”Trade entry price: >>> {trade_entry_price} <<<")
print(f”Signal Strength: =>> {signal_strength} <<= ")
print(f"Stop-Loss price: <span class="resistance"> {stop_loss_price} </span>")
print(f"Take-Profit price: <span class="support"> {take_profit_price} </span>")
`
Summary
- Latest Close: 4902.67
- Latest MA(5): [Calculated value]
- Latest BB Upper: [Calculated value]
- MA(5) Trending Downward: [True/False]
- Support Level: [Calculated value]
- Resistance Level: [Calculated value]
Based on the above calculations and analysis, the final trading signal is:
- Direction signal: [Watch/Short]
- Latest Close: >>> 4902.67 <<<
- Signal Strength: =>> [0/-5] <<=
- Support level: +>> [Calculated value] <<+
- Resistance level: ->> [Calculated value] <<-